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Estimating the effects of parameter variability on learning curve model predictions

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  • Vigil, Dimas P.
  • Sarper, Huseyin

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  • Vigil, Dimas P. & Sarper, Huseyin, 1994. "Estimating the effects of parameter variability on learning curve model predictions," International Journal of Production Economics, Elsevier, vol. 34(2), pages 187-200, March.
    • Handle: RePEc:eee:proeco:v:34:y:1994:i:2:p:187-200
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    Cited by:

    1. Lafond, François & Bailey, Aimee Gotway & Bakker, Jan David & Rebois, Dylan & Zadourian, Rubina & McSharry, Patrick & Farmer, J. Doyne, 2018. "How well do experience curves predict technological progress? A method for making distributional forecasts," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 104-117.
    2. Yamane, Yasuo & Takahashi, Katsuhiko & Hamada, Kunihiro & Morikawa, Katsumi & Nur Bahagia, Senator & Diawati, Lucia & Cakravastia, Andi, 2015. "Developing a plant system prediction model for technology transfer," International Journal of Production Economics, Elsevier, vol. 166(C), pages 119-128.
    3. Thomas Boucher & Yuchen Li, 2016. "Technical note: systematic bias in stochastic learning," International Journal of Production Research, Taylor & Francis Journals, vol. 54(11), pages 3452-3463, June.
    4. Ranasinghe, Thilini & Senanayake, Chanaka D. & Grosse, Eric H., 2024. "Effects of stochastic and heterogeneous worker learning on the performance of a two-workstation production system," International Journal of Production Economics, Elsevier, vol. 267(C).

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